Favorite Amazon SageMaker Pipelines is a fully managed AWS service for building and orchestrating machine learning (ML) workflows. SageMaker Pipelines offers ML application developers the ability to orchestrate different steps of the ML workflow, including data loading, data transformation, training, tuning, and deployment. You can use SageMaker Pipelines to orchestrate
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Shared by AWS Machine Learning September 7, 2023
Favorite In February 2022, Amazon Web Services added support for NVIDIA GPU metrics in Amazon CloudWatch, making it possible to push metrics from the Amazon CloudWatch Agent to Amazon CloudWatch and monitor your code for optimal GPU utilization. Since then, this feature has been integrated into many of our managed
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Shared by AWS Machine Learning September 7, 2023
Favorite Efficient control policies enable industrial companies to increase their profitability by maximizing productivity while reducing unscheduled downtime and energy consumption. Finding optimal control policies is a complex task because physical systems, such as chemical reactors and wind turbines, are often hard to model and because drift in process dynamics
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Shared by AWS Machine Learning September 7, 2023
Favorite A license-review project has been underway with the goal of creating a systematic and well-ordered database of all the licenses that have been submitted to OSI for approval since the time of the organization’s founding. Giulia Dellanoce was brought on as an intern to complete this Approval Registry project,
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Shared by voicesofopensource September 7, 2023
Favorite Multi-model endpoints (MMEs) are a powerful feature of Amazon SageMaker designed to simplify the deployment and operation of machine learning (ML) models. With MMEs, you can host multiple models on a single serving container and host all the models behind a single endpoint. The SageMaker platform automatically manages the
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Shared by AWS Machine Learning September 6, 2023
Favorite Today, we are excited to announce the capability to fine-tune Llama 2 models by Meta using Amazon SageMaker JumpStart. The Llama 2 family of large language models (LLMs) is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Fine-tuned
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Shared by AWS Machine Learning September 6, 2023
Favorite Amazon Kendra is an intelligent search service powered by machine learning (ML). With Amazon Kendra, you can easily aggregate content from a variety of content repositories into an index that lets you quickly search all your enterprise data and find the most accurate answer. Adobe Experience Manager (AEM) is
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Shared by AWS Machine Learning September 6, 2023
Favorite Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. It’s powered by large language models (LLMs) that are pre-trained on vast amounts of data and commonly referred to as foundation models (FMs). With the advent of these
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Shared by AWS Machine Learning September 6, 2023
Favorite Posted by Si-An Chen, Student Researcher, Cloud AI Team, and Chun-Liang Li, Research Scientist, Cloud AI Team Time series forecasting is critical to various real-world applications, from demand forecasting to pandemic spread prediction. In multivariate time series forecasting (forecasting multiple variants at the same time), one can split existing
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Shared by Google AI Technology September 6, 2023
Favorite Since Google was founded, we’ve worked to answer hard questions, help people get answers to theirs, and move technology forward for the world. View Original Source (blog.google/technology/ai/) Here.
Shared by AWS Machine Learning September 7, 2023
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